Abstract
Water resource is considered as a significant factor in the development of regional environment and society. Water consumption prediction can provide an important decision basis for the regional water supply scheduling optimizations. According to the periodicity and randomness nature of the daily water consumption data, a Markov modified autoregressive moving average (ARIMA) model was proposed in this study. The proposed model, combined with the Markov chain, can correct the prediction error, reduce the continuous superposition of prediction error, and improve the prediction accuracy of future daily water consumption data. The daily water consumption data of different monitoring points were used to verify the effectiveness of the model, and the future water consumption was predicted in the study area. The results show that the proposed algorithm can effectively reduce the prediction error compared to the ARIMA.
Highlights
Water resources are considered as an important key factor for regional sustainable development in both developing and developed countries
Sebri [25] compared the performance of Box and Jenkins’ autoregressive moving average (ARIMA) model and artificial neural network (ANN) model on water consumption prediction in Tunisia, and the result indicated that the traditional Box–Jenkins method outperformed ANN estimated on raw, degraded, or deseasonalized data in terms of forecasting accuracy
Water resource is an important factor affecting the sustainable development of regional environment and society
Summary
Water resources are considered as an important key factor for regional sustainable development in both developing and developed countries. Sebri [25] compared the performance of Box and Jenkins’ ARIMA model and ANN model on water consumption prediction in Tunisia, and the result indicated that the traditional Box–Jenkins method outperformed ANN estimated on raw, degraded, or deseasonalized data in terms of forecasting accuracy. It is difficult to obtain the seasonal and periodic characteristics of water consumption data by ANN, and it is easy to produce over fitting problems in the limited dataset for a strong nonlinear approximation ability [26], which reduces the prediction accuracy. It is worth performing a further study about the ARIMA model for predicting water consumption data. Aiming at the prediction error, this study proposes a prediction value correction method that is based on Markov chain
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